Skip to main content
Glama

LiveDataLink

by io.github.blackboxfoundry

nrel_utility_rates

Read-onlyIdempotent

Average residential, commercial, and industrial electric utility rates (cents per kWh) for a location, plus the utility name. Used for ROI analysis on solar, EV charging, building electrification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude in decimal degrees. Use with lon as alternative to address.
lonNoLongitude in decimal degrees. Use with lat as alternative to address.
addressNoStreet address, city/state, or place name. Either address OR lat+lon required.

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful output context (average rates by customer class and utility name) but does not disclose behavioral details such as how location is resolved, potential data gaps, or response structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasteful wording. It front-loads the core output (average rates plus utility name) and follows with a concise use-case statement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple lookup tool with fully documented parameters and safety annotations, the description is reasonably complete. It specifies the output fields and units even in the absence of an output schema, and it gives the intended application context. Minor missing detail about the exact response structure prevents a 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameters lat, lon, and address are already documented with enough detail, including the address-or-lat/lon alternative. The description adds only the general notion of 'for a location' and does not provide additional parameter semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the resource (electric utility rates) and the specific data returned: average residential, commercial, and industrial rates in cents per kWh plus utility name. It is specific enough to be useful, but it does not explicitly distinguish itself from related energy/electricity sibling tools like eia_electricity_state or nrel_solar_resource.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when the tool is relevant: 'Used for ROI analysis on solar, EV charging, building electrification.' This helps an agent identify suitable scenarios, though it does not mention when not to use it or point to alternative energy-rate tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All LiveDataLink toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources